Park Gregory, Yaden David Bryce, Schwartz H Andrew, Kern Margaret L, Eichstaedt Johannes C, Kosinski Michael, Stillwell David, Ungar Lyle H, Seligman Martin E P
Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Computer Science Department, Stony Brook University, Stony Brook, New York, United States of America.
PLoS One. 2016 May 25;11(5):e0155885. doi: 10.1371/journal.pone.0155885. eCollection 2016.
Using a large social media dataset and open-vocabulary methods from computational linguistics, we explored differences in language use across gender, affiliation, and assertiveness. In Study 1, we analyzed topics (groups of semantically similar words) across 10 million messages from over 52,000 Facebook users. Most language differed little across gender. However, topics most associated with self-identified female participants included friends, family, and social life, whereas topics most associated with self-identified male participants included swearing, anger, discussion of objects instead of people, and the use of argumentative language. In Study 2, we plotted male- and female-linked language topics along two interpersonal dimensions prevalent in gender research: affiliation and assertiveness. In a sample of over 15,000 Facebook users, we found substantial gender differences in the use of affiliative language and slight differences in assertive language. Language used more by self-identified females was interpersonally warmer, more compassionate, polite, and-contrary to previous findings-slightly more assertive in their language use, whereas language used more by self-identified males was colder, more hostile, and impersonal. Computational linguistic analysis combined with methods to automatically label topics offer means for testing psychological theories unobtrusively at large scale.
利用一个大型社交媒体数据集以及计算语言学中的开放词汇方法，我们探究了不同性别、所属群体和 assertiveness 在语言使用上的差异。在研究 1 中，我们分析了来自 52000 多名脸书用户的 1000 万条信息中的主题（语义相似的词群）。大多数语言在性别上差异不大。然而，与自我认定为女性的参与者最相关的主题包括朋友、家庭和社交生活，而与自我认定为男性的参与者最相关的主题包括咒骂、愤怒、对事物而非人的讨论以及辩论性语言的使用。在研究 2 中，我们沿着性别研究中普遍存在的两个人际维度——所属群体和 assertiveness，绘制了与男性和女性相关的语言主题。在一个超过 15000 名脸书用户的样本中，我们发现所属群体语言的使用存在显著的性别差异，而 assertive 语言存在细微差异。自我认定为女性更多使用的语言在人际方面更温暖、更有同情心、更礼貌，并且——与之前的研究结果相反——在语言使用上略显更 assertive，而自我认定为男性更多使用的语言则更冷漠、更具敌意且缺乏人情味。计算语言学分析与自动标注主题的方法相结合，为大规模地悄然测试心理学理论提供了手段。
